ethminer_fpga
ArrayFire
| ethminer_fpga | ArrayFire | |
|---|---|---|
| 1 | 6 | |
| 41 | 4,413 | |
| - | 0.5% | |
| 1.8 | 7.1 | |
| about 3 years ago | 28 days ago | |
| C++ | C++ | |
| GNU General Public License v3.0 only | BSD 3-clause "New" or "Revised" License |
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ethminer_fpga
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FPGA mining ?
Hey, I have a ultra96 board with a zynq ultrascale FPGA laying in a box and i am looking for some project to use it for. As I am also interested in crypto i thought it might be cool to set it up as a miner, and in my research I found this project for a different board: https://github.com/mkhaled87/ethminer_fpga I realize ether will soon move to PoS and my work will become obsolete, but I view it as learning with the added bonus of mining some coins. Do anyone here mine Ethereum on FPGA?
ArrayFire
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Learn WebGPU
Loads of people have stated why easy GPU interfaces are difficult to create, but we solve many difficult things all the time.
Ultimately I think CPUs are just satisfactory for the vast vast majority of workloads. Servers rarely come with any GPUs to speak of. The ecosystem around GPUs is unattractive. CPUs have SIMD instructions that can help. There are so many reasons not to use GPUs. By the time anyone seriously considers using GPUs they're, in my imagination, typically seriously starved for performance, and looking to control as much of the execution details as possible. GPU programmers don't want an automagic solution.
So I think the demand for easy GPU interfaces is just very weak, and therefore no effort has taken off. The amount of work needed to make it as easy to use as CPUs is massive, and the only reason anyone would even attempt to take this on is to lock you in to expensive hardware (see CUDA).
For a practical suggestion, have you taken a look at https://arrayfire.com/ ? It can run on both CUDA and OpenCL, and it has C++, Rust and Python bindings.
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seeking C++ library for neural net inference, with cross platform GPU support
What about Arrayfire. https://github.com/arrayfire/arrayfire
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[D] Deep Learning Framework for C++.
Low-overhead — not our goal, but Flashlight is on par with or outperforming most other ML/DL frameworks with its ArrayFire reference tensor implementation, especially on nonstandard setups where framework overhead matters
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[D] Neural Networks using a generic GPU framework
Looking for frameworks with Julia + OpenCL I found array fire. It seems quite good, bonus points for rust bindings. I will keep looking for more, Julia completely fell off my radar.
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Arrayfire progressive performance decline?
Your Problem may be the lazy evaluation, see this issue: https://github.com/arrayfire/arrayfire/issues/1709
What are some alternatives?
ethminer - Maetti's Fork (Ethereum) + Altera/Intel OpenCL(FPGA)
Thrust - [ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
waifu2x-converter-cpp - Improved fork of Waifu2X C++ using OpenCL and OpenCV
Boost.Compute - A C++ GPU Computing Library for OpenCL
oneDNN - oneAPI Deep Neural Network Library (oneDNN)
VexCL - VexCL is a C++ vector expression template library for OpenCL/CUDA/OpenMP
triSYCL - Generic system-wide modern C++ for heterogeneous platforms with SYCL from Khronos Group
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
ea - Ethereum (ethash) miner with SYCL (HIP, CUDA, Intel GPUs, OpenMP,...), OpenCL, CUDA and stratum support
CUB - THIS REPOSITORY HAS MOVED TO github.com/nvidia/cub, WHICH IS AUTOMATICALLY MIRRORED HERE.
Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System
moderngpu - Patterns and behaviors for GPU computing